Predicting Soybean Growth and Drought Survival with Genetics

Jim Crocker
11th March, 2024

Predicting Soybean Growth and Drought Survival with Genetics

Image Source: Natural Science News, 2024

Key Findings

  • Tokyo researchers developed models to predict soybean growth using genetic and environmental data
  • The models, using aerial drones, showed how soybeans react to drought by analyzing canopy area and height
  • While not more accurate than single-trait predictions, the models revealed how genetics and environment interact, especially in later growth stages
Understanding the intricacies of plant growth and how it interacts with the environment is crucial for improving crop yields and breeding more resilient plants. A recent study from The University of Tokyo[1] has developed models that aim to predict daily growth in soybeans by considering both genetic makeup and environmental factors. This research builds upon previous attempts to understand plant growth, integrating modern technology and statistical methods to offer a more nuanced view of how soybeans grow under different conditions. Soybean cultivation is a significant agricultural activity worldwide, but it faces challenges such as drought stress, which can severely impact growth and yield. To address this, scientists have turned to high-throughput phenotyping systems, which allow for the rapid assessment of plant characteristics over time. These systems have become more accessible thanks to advancements in technology, such as unmanned aerial vehicles (UAVs) that can capture detailed growth data from above the fields[2][3]. The study in question applied a novel approach to this problem by using a reaction norm model, which is a way of quantifying how different genotypes respond to varying environmental conditions. This model was specifically designed to analyze plant growth data and included sophisticated statistical tools like spline and random forest models. These tools help to break down complex data into understandable patterns, allowing researchers to see how daily growth is influenced by the environment of the previous day. Researchers focused on soybean canopy area and height, essential indicators of overall plant health and productivity. They measured these traits in 198 different soybean cultivars using UAVs and then analyzed how the plants reacted to various levels of drought stress. Soil moisture was tracked to provide a detailed picture of the environmental conditions affecting the plants. The models' performance was assessed through cross-validation schemes, which are methods to evaluate how well a predictive model will perform in practice. While the accuracy of these new models did not exceed that of single-trait genomic prediction, which looks at one characteristic at a time, they showed promise in capturing the genotype-by-environment (G × E) interactions, particularly during the later growth stages when using the random forest model[4]. One of the key findings was the ability to visualize significant variations in G × E interactions for canopy height during the early growth period, using the spline model. This visualization is a step forward in understanding how different genetic factors respond to environmental stresses at various plant growth stages. It's important to note that the integration of environmental data, like weather conditions, has been previously explored. However, incorporating such data directly into G × E models has not always improved predictive ability[4]. This study's approach represents an evolution in modeling plant growth, aiming to connect environmental conditions with plant development more effectively. Moreover, the study aligns with broader research efforts to tackle the challenges of climate change and the complex interactions between genotype, environment, and management (G × E × M)[5]. As climate change introduces new variables into the agricultural equation, models that can predict plant growth and yield in a changing world become increasingly valuable. In conclusion, the research from The University of Tokyo contributes to the ongoing effort to optimize crop breeding and management strategies. By applying cutting-edge statistical models to high-resolution growth data, the study offers insights into the dynamic relationship between a plant's genetic makeup and its environment. While there is still room for improvement in predictive accuracy, these models represent a significant step toward more resilient and productive crops in the face of environmental challenges.

GeneticsPlant ScienceAgriculture

References

Main Study

1) Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean.

Published 9th March, 2024

https://doi.org/10.1007/s00122-024-04565-5


Related Studies

2) Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations.

https://doi.org/10.3389/fpls.2022.828864


3) Genomic prediction modeling of soybean biomass using UAV-based remote sensing and longitudinal model parameters.

https://doi.org/10.1002/tpg2.20157


4) Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project.

https://doi.org/10.3389/fgene.2020.592769


5) Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity.

https://doi.org/10.1007/s00122-021-03812-3



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